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Changing the train.py entry point to handle a diffusion model instead in train_diffusion.py.
This trains the model end-to-end on real data!
examples/local/run_diffusion.sh gives an example on how to do this
Reorganization of the config file to handle the different arguments as cleanly as possible. We should check the library simple_parsing in the future to clean up even more.
Rewrite the load_model function - some cleaning will be required in the future if we integrate simple_parsing.
Harmonize the names of variables between the dataloader and the model.
Training a model requires about 45 minutes of preprocessing at the moment. This is only done once - to create .parquet file that are parsed by the library Datasets.
TODO: integrate orion and more metrics in tensorboard